Learning intended user actions

ABSTRACT

A method and system are provided. The method includes receiving, by a microphone and camera, user utterances indicative of user commands and associated user gestures for the user utterances. The method further includes parsing, by a hardware-based recognizer, sample utterances and the user utterances into verb parts and noun parts. The method also includes recognizing, by a hardware-based recognizer, the user utterances and the associated user gestures based on the sample utterances and descriptions of associated supporting gestures for the sample utterances. The recognizing step includes comparing the verb parts and the noun parts from the user utterances individually and as pairs to the verb parts and the noun parts of the sample utterances. The method additionally includes selectively performing a given one of the user commands responsive to a recognition result.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application of co-pending U.S. patentapplication Ser. No. 14/623,233, filed on Feb. 16, 2015, which isincorporated herein by reference in its entirety.

BACKGROUND

1. Technical Field

The present invention relates generally to information processing and,in particular, to the fields of speech and gesture recognition.

2. Description of the Related Art

Work involving resolving anaphora (where, as used herein, anaphorarefers to pronouns) in a multimodal environment is rule-based and doesnot employ learning. Accordingly, such prior art is therefore static andbrittle. That is, such prior art is brittle in the sense that voicetranscription applied to terse assertions is generally of very poorquality and, hence, rules that depend in part on accurate ornear-accurate transcription of words can be failure prone. Thus, thereis a need for a more dynamic and non-ruled based approach to multimodalcommand recognition capable of learning and resolving anaphora.

SUMMARY

According to an aspect of the present principles, a method is provided.The method includes receiving, by a microphone and camera, userutterances indicative of user commands and associated user gestures forthe user utterances. The method further includes parsing, by ahardware-based recognizer, sample utterances and the user utterancesinto verb parts and noun parts. The method also includes recognizing, bya hardware-based recognizer, the user utterances and the associated usergestures based on the sample utterances and descriptions of associatedsupporting gestures for the sample utterances. The recognizing stepincludes comparing the verb parts and the noun parts from the userutterances individually and as pairs to the verb parts and the nounparts of the sample utterances. The method additionally includesselectively performing a given one of the user commands responsive to arecognition result.

According to another aspect of the present principles, a system isprovided. The system includes a microphone and camera for receiving userutterances indicative of user commands and associated user gestures forthe user utterances. The system further includes a hardware-basedrecognizer for parsing sample utterances and the user utterances intoverb parts and noun parts, and recognizing the user utterances and theassociated user gestures based on the sample utterances and descriptionsof associated supporting gestures for the sample utterance by comparingthe verb parts and the noun parts from the user utterances individuallyand as pairs to the verb parts and the noun parts of the sampleutterances. The system also includes a user command selective executiondevice for selectively performing a given one of the user commandsresponsive to a recognition result.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 shows an exemplary processing system 100 to which the presentprinciples may be applied, in accordance with an embodiment of thepresent principles;

FIG. 2 shows an exemplary system 200 for learning intended user actionsutilizing speech and gesture recognition, in accordance with anembodiment of the present principles;

FIGS. 3-4 show an exemplary method 300 for learning intended useractions utilizing speech and gesture recognition, in accordance with anembodiment of the present principles;

FIG. 5 shows an exemplary cloud computing node 510, in accordance withan embodiment of the present principles;

FIG. 6 shows an exemplary cloud computing environment 650, in accordancewith an embodiment of the present principles; and

FIG. 7 shows exemplary abstraction model layers, in accordance with anembodiment of the present principles.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present principles are directed to learning intended user actions.Embodiments of the present principles learn intended user actionsutilizing speech and gesture recognition.

In an embodiment, the present principles involve interactive machinelearning (also known in the literature as online machine learning) ofmultimodal commands. In an embodiment, the present principles can beimplemented as a system or method for spoken language and gesturalrecognition. The user can make gestures and/or issue speech commands infront of a gesture recognition device to execute a command. At thetrigger of the command by a user, the system and method can provide realtime feedback. Each natural language command can be broken down into averb and noun and the system can learn to associate the action with theverb and the noun separately in combination with the gesture or togethercombined with the gesture. Furthermore, the system and method can learnnew ways of articulating and gesticulating intent though positive (i.e.,user accepted) and negative (i.e., user rejected) examples.

Advantageously, the present principles can be applied to many situationsnot capable of being addressed in an acceptable manner, if at all, bythe prior art. For example, in an embodiment, the present principles canovercome the following problem: a user intends that a desired action becarried out by a computer system and/or other processor-enabled devicein response to using a combination of speech and gestural actions, butbecause of the very terse typical utterance and use of anaphora(pronouns), it is not easy for the computer system and/or device toaccurately transcribe the spoken words and thereby understand theintended behavior of the user. Embodiments of the present principles canreadily provide the computer system with the capability to processanaphora in such a situation. These and other advantages of the presentprinciples as well as situations to which the present principles can beapplied are readily determined by one of ordinary skill in the art,given the teachings of the present principles provided herein, whilemaintaining the spirit of the present principles.

Moreover, the present principles are advantageously adept at processingdeixis (deixis herein refers to words and phrases that cannot be fullyunderstood without additional spatial and/or contextual information). Assuch, phrases that can be difficult to understand without additionalspatial and/or contextual information can be readily processed inaccordance with the present principles.

Further, while one or more embodiments are directed to pointing as agesture to which the present principles are applied, the presentprinciples are not limited to solely pointing and, thus, other gestures(for example flicking, pushing or pulling gestures) can also be used inaccordance with the teachings of the present principles, whilemaintaining the spirit of the present principles.

FIG. 1 shows an exemplary processing system 100 to which the presentprinciples may be applied, in accordance with an embodiment of thepresent principles. The processing system 100 includes at least oneprocessor (CPU) 104 operatively coupled to other components via a systembus 102. A cache 106, a Read Only Memory (ROM) 108, a Random AccessMemory (RAM) 110, an input/output (I/O) adapter 120, a sound adapter130, a network adapter 140, a user interface adapter 150, and a displayadapter 160, are operatively coupled to the system bus 102.

A first storage device 122 and a second storage device 124 areoperatively coupled to system bus 102 by the I/O adapter 120. Thestorage devices 122 and 124 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid state magnetic device,and so forth. The storage devices 122 and 124 can be the same type ofstorage device or different types of storage devices.

A speaker 132 is operatively coupled to system bus 102 by the soundadapter 130. A transceiver 142 is operatively coupled to system bus 102by network adapter 140. A display device 162 is operatively coupled tosystem bus 102 by display adapter 160.

A first user input device 152, a second user input device 154, and athird user input device 156 are operatively coupled to system bus 102 byuser interface adapter 150. The user input devices 152, 154, and 156 canbe any of a keyboard, a mouse, a keypad, an image capture device, amotion sensing device, a microphone, a device incorporating thefunctionality of at least two of the preceding devices, and so forth. Ofcourse, other types of input devices can also be used, while maintainingthe spirit of the present principles. The user input devices 152, 154,and 156 can be the same type of user input device or different types ofuser input devices. The user input devices 152, 154, and 156 are used toinput and output information to and from system 100.

Of course, the processing system 100 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, various other input devicesand/or output devices can be included in processing system 100,depending upon the particular implementation of the same, as readilyunderstood by one of ordinary skill in the art. For example, varioustypes of wireless and/or wired input and/or output devices can be used.Moreover, additional processors, controllers, memories, and so forth, invarious configurations can also be utilized as readily appreciated byone of ordinary skill in the art. These and other variations of theprocessing system 100 are readily contemplated by one of ordinary skillin the art given the teachings of the present principles providedherein.

Moreover, it is to be appreciated that system 200 described below withrespect to FIG. 2 is a system for implementing respective embodiments ofthe present principles. Part or all of processing system 100 may beimplemented in one or more of the elements of system 200.

Further, it is to be appreciated that processing system 100 may performat least part of the method described herein including, for example, atleast part of method 300 of FIGS. 3-4. Similarly, part or all of system200 may be used to perform at least part of method 300 of FIGS. 3-4.

FIG. 2 shows an exemplary system 200 for learning intended user actionsutilizing speech and gesture recognition, in accordance with anembodiment of the present principles. The system 200 includes a gesturerecognition device 210, a verb (action word) recognizer 220, a noun andpronoun recognizer 230, a part of speech to user gesture associationdevice 240, a feedback generator 250, a confidence indication generator260, a user command selective execution device 270, a memory device 280,an utterance extender 281, a probability of action determination device290, and an arbiter 295. The gesture recognition device 210, the verbrecognizer 220, the noun and pronoun recognizer 230, the utteranceextender 281, the part of speech to user gesture association device 240,and the memory device 280 form a user speech and associated gesturerecognizer 298. The user speech and associated gesture recognizer 299,the feedback generator 250, and the confidence indication generator 260form a user command learning device 299.

The gesture recognition device 210 recognizes actions and/or gestures(hereinafter “gestures”) performed by a user. In an embodiment, thegesture recognition device 210 includes a motion capture device 211, agesture database 212, and a user gesture to stored gesture comparisondevice 213. In an embodiment, the motion capture device 211 can includea camera, a camcorder, or any other type of image capture device. Whiledescribed as a motion capture device, element 211 can be any type ofdevice capable of capturing a user gesture, including static gestureswhere no or minimal movement is performed by the user. In an embodiment,the gesture database 212 stores expected user gestures and can besupplemented by new (e.g., previously unstored) gestures as they arecaptured and used by system 200. The user gesture to stored gesturecomparison device 213 compares contemporaneously performed user gesturesto gestures in the gesture database 212 in order to recognize thecontemporaneously performed user gestures. In an embodiment, the usergesture to stored gesture comparison device 213 can provide an outputindicative of a particular recognized gesture (e.g., an outputindicative of the user pointing and where the user is pointing [akin toa mouse pointer], and so forth).

The verb recognizer 220 recognizes verbs from utterances spoken by theuser.

The noun and pronoun recognizer 230 recognizes nouns and pronouns fromutterances spoken by a user.

The part of speech to user gesture association device 240 associatesparts of speech (e.g., verbs, nouns, and pronouns) to user gestures.

The feedback manager 250 records and/or manages feedback (also referredto herein as evidence) relating to whether a pair of {action word,noun/pronoun} together with an associated gesture indicates that a userexpects a particular system response and/or relating to whether any oneof the {action word or noun/pronoun} together with an associated gestureindicates that a user expects a particular system response.

A confidence indication generator 260 generates confidence indications(which can take any form, including a score) relating to whether a pairof {action word, noun/pronoun} together with an associated gestureindicates that a user expects a particular system response and/orrelating to whether any one of the {action word or noun/pronoun}together with an associated gesture indicates that a user expects aparticular system response. In an embodiment, the confidence indicationgenerator 260 can include an error indication generator 261 thatrepresents a degree of error or uncertainty in the system's confidencethat a pair of {action word, noun/pronoun} together with an associatedgesture are indicative of a particular system response and/or a degreeof error or uncertainly in the system's confidence that any of the{action word or noun/pronoun} together with an associated gesture areindicative of a particular system response. In an embodiment, one ormore of the confidence indications and the error indications can beused. In an embodiment, one or both of the confidence indications andthe error indications can be determined responsive to the feedbackrecorded and/or managed by the feedback manager 250. In an embodiment,the confidence indications and/or the error indications can be generatedresponsive to a predetermined number of times that a pair of {actionword, noun/pronoun} and/or any one of the {action word or noun/pronoun}are deemed to be successfully implemented (i.e., in response to theuser's request to take an action, the system responds and the user doesnot immediately attempt to undo the action.).

The user command selective execution device 270 selectively executesuser requests for system response. The selectivity of execution of thesesystem responses can depend on one or more criteria. Such criteriainclude, but are not limited to, evidence (e.g., supporting evidence,contrary evidence), user intent (e.g., as represented byaffirmance/allowance of an action by a user or a request to undo anaction by the user), and so forth. In an embodiment, the evidence caninclude feedback and/or confidence indications and/or error indications.

The memory device 280 stores evidence for particular combinations ofaction words potentially spoken in combination with a noun/pronoun inthe presence of a recognized user gesture. In an embodiment, the memorydevice 280 stores “seed expressions” that may accompany a given gesture(e.g., the gestures stored in the gesture database 212) that areindicative of particular, expected, system responses. These seedexpressions can be used to build up an ever increasing lexicon throughpositive and negative examples as indicated through evidence (e.g.,feedback, confidence indications, and/or error indications). In anembodiment, the memory device 280 can include a statistical knowledgerepository 283 that keeps track of words and phrases used in conjunctionwith recognized gestures, and how often each of these lead to acceptedsystem actions versus system actions which are rejected via an undorequest.

The utterance extender 281 can extend sample utterances (e.g., seedexpressions) prior to or in parallel with training. Such extensions canbe based on synonyms, common known mistranscriptions of either thesample utterances or their synonyms, or mistranscriptions that arewitnessed by the system. Of course, other ways of extension can also beused, while maintaining the spirit of the present principles.

The probability of action determination device 290 makes adetermination, given a combination of words and gesture, the respectiveprobabilities or likelihoods that particular system actions have beenrequested.

The multiple action arbiter 295 arbitrates between the multiple systemactions when two or more are deemed to be applicable at a given time fora given session with a user. The arbiter can then pick one from amongseveral of the multiple actions and, in an embodiment, can pick two ormore, but less than all or even up to all, of the actions as they aredeemed applicable. The determination of applicability can be based onthe feedback and/or the confidence indications and/or the errorindications.

In the embodiment shown in FIG. 2, the elements thereof areinterconnected by a bus 201. However, in other embodiments, other typesof connections can also be used. Moreover, in an embodiment, at leastone of the elements of system 200 is processor-based. Further, while thefeedback generator 250 and confidence indication generator 260 are shownas separate elements, in other embodiments, these elements can becombined as one element. The converse is also applicable, where anelement included in another element in FIG. 2 can be implemented as aseparate element in another embodiment. These and other variations ofthe elements of system 200 are readily determined by one of ordinaryskill in the art, given the teachings of the present principles providedherein, while maintaining the spirit of the present principles.

FIGS. 3-4 show a method 300 for learning intended user actions utilizingspeech and gesture recognition, in accordance with an embodiment of thepresent principles.

At step 305, choreograph a multimodal task to be supported.

At step 310, specify (A) seed commands including the following twopairs: {action word, noun/pronoun for source} and {action word, noun,pronoun for target}, and (B) whether the source and target requirepointing for disambiguation. Typically if a pronoun is used pointing isrequired for disambiguation of that pronoun.

At step 315, add seed commands to a statistical knowledge repository 283as valid source pairs and valid source individuals. The statisticalknowledge repository 283 will keep track of words and word pairs used inconjunction with recognized gestures, and how often each of these leadto accepted system actions versus system actions which are then rejectedvia an undo request.

At step 320, recognize, from the beginning of an utterance by a user,the anticipated gestural action (typically pointing) and at least one ofthe {action word, noun/pronoun for source}. This step is the selectionphase of the command. A prototypical example is a user utterance of theform “Take this” while the user points to whatever “this” refers to. Inthis case, the action word is “Take”, the pronoun for the source is“this” and the anticipated gestural action is pointing.

At step 325, generate an assumption that the user intends to select agiven object (that is, issue a selection command), and provide anindication of the target of the selection (e.g., by highlighting thegiven object on a screen).

At step 330, determine whether or not the user intends to proceed withthe selection command. If so, then the method proceeds to step 335.Otherwise, for example, if the user asks to undo the command, the methodproceeds to step 332 to record the negative example in the statisticalknowledge repository 283, and returns to step 320.

At step 335, register, in the statistical knowledge repository 383, thefact that the pair is valid and that the individual action word and nounor pronoun are valid in conjunction with the given gesture.

At step 340, capture, from the user, a remainder of the utterance with arecognized gestural action (typically pointing) and at least one of the{action word, noun/pronoun for target}.

At step 345, generate an assumption that the user intends for theprescribed action to be implemented and implement the prescribed action.

At step 350, determine whether or not the user is continuing with theinteraction (e.g., making further utterances, gesturing, interactingwith others) or intends that the prescribed action be undone. If theuser is continuing with the interaction, the method proceeds to step355. Otherwise, if the user intends that the prescribed action be undonethe method continues to step 352 wherein the statistical knowledgerepository registers the fact that the pair is not valid and that theindividual action word and noun or pronoun are not valid in conjunctionwith the given gesture, and then the method returns to step 340.

At step 355, record a positive instance of {action word, noun/pronounfor target} and positive instances of individuals {action word},{noun/pronoun for target} in the statistical knowledge repository 383.

Our solution learns the different ways in which the desire for a givenaction, or system response, can be expressed, starting with one or more“seed expressions” for this action or system response, building up anever-expanding lexicon through both positive and negative examples. Inan embodiment, we exploit the following: the utterance behind anysupported gestural action necessarily has two parts. For example, theexpression “take this and put it there” has the two parts (1) “takethis” and (2) “[and] put it there”, where one first points to whatever“this” is, and then points to whatever “that” is. At each juncture, thesystem gives feedback that it has understood what the user intends.Thus, if the “this” in the clause “take this” is an image on one screen,the moment “take this” is uttered with an accompanying gesture, thesystem highlights “this” to indicate its understanding of this and alsothat the user wishes to select “this” and do something with it.Following the expression “take this”, when the user points in thedirection of “there” a mouse-like cursor appears on the surface to whichthe user is pointing to give feedback. Only when the user is sure thesystem understands where the user is pointing is the user expected tolock in on the expression “and move it there”, at which point the imageoriginally in the “this” location is moved to the “there” location. Ineach segment of the utterance, the system looks for the user to employan action word (verb) that the system recognizes and either a pronoun ornoun that the system recognizes. If only one of the two (action word, ornoun/pronoun) is present, then the system will still execute the actionunless the system has accumulated evidence to the contrary, i.e., thatthe single action word or single noun does not generally indicate anintent to select the thing being pointed at. The user is alwayspresented with an undo option, by saying a phrase like “no” or “no, Ididn't mean that” or any word or words that convey the same concept ofwanting to undo something. If the user is satisfied with the move, thenthe system learns a positive instance of the given new action word ornoun/pronoun. If the user is unsatisfied, then a negative instance iscredited to the given new action word or noun/pronoun. If a noun/pronounor action word is successful a majority of the time, it is assumed to bevalid with some confidence and error bars. The greater the error bars,the less the system will trust its conclusion, and especially in thecase where an action word, noun/pronoun pair is deemed to onlymarginally be negatively indicative of an intent to select something. Inan embodiment, addition probing will be done in an effort to narrow theerror bars (in an exploration versus exploitation fashion).

In an embodiment, there can be probabilistic extensions to the presentprinciples in the case that an implementation (e.g., system, method, andso forth) of the present principles is actively listening for multipleactions. For example, the system may be listening for the possibilitythat the user wants to move something to a new screen or alternativelyadd something to a shopping basket, or to a table of some sort forfurther analysis. In an embodiment, different and multiple listeners canbe enabled at any given time as applicable or intended. The listenerbehind each action will have its own probability of acting, with eachtrading off exploration versus exploitation in bandit-like fashion. Forexample, the listener listening for the possibility that the user wantsto move a selected item to a secondary screen may have X % confidencethat the last utterance and accompanying gesture was indicative of thisaction, while the listener listening for something to be added to theshopping cart may have Y % confidence that the last utterance wasindicative of this action, and the listener listening for something tobe added to a given table may have Z % confidence. Each listener learnssomething by being wrong, with the least used listeners generallystanding to gain the most. Thus, with some probability, each listenerwill opt to act, even when their assessment of their probability ofbeing correct in their action is less than 50%. If several listenersdecide to act there will be an arbitration. Each submits a confidence inits action and the one with highest confidence will be given the chanceto act (assuming the actions are mutually exclusive, e.g. a move requestand a delete request). In an embodiment, multiple and differentlisteners can be implemented by different and multiple threads,processes and/or devices being executed or being used concurrently inorder to recognize and process the multiple actions.

In addition to seeding the system with sample utterances, the system mayuse various means of extending the sample utterances prior to or inparallel with training.

Consider the utterance “Select this and move it there”. The systembreaks this utterance into two pieces: (i) “Select this,” and (ii) “moveit there,” the former being what we call the selection directive, andthe latter the move directive. The in-between word “and” is ignored. Inan embodiment, up to two filler words of this sort are allowed. Each ofthese directives is deemed to be completed when the user is pointing andeither a deictic is heard or a verb is heard (but no deictic) and apause in speech occurs of at least 0.25 seconds. To estimate theprobability, p_(accept), that the user actually means to selectsomething, under the assumption that the system believes the user to bepointing at something that is selectable, in a sample embodiment thesystem uses the following formula:

$\begin{matrix}{p_{accept} = \frac{p_{v} + p_{p} + {\frac{{v,p}}{2}p_{v,p}}}{2 + {\frac{v,p}{2}}}} & (1)\end{matrix}$

where: p_(v)=probability of acceptance of the utterance given that thesystem has seen the verb; p_(p)=probability of acceptance of theutterance given that the system has seen the pronoun;p_(v,p)=probability of acceptance of the utterance given that the systemhas seen both the verb and the pronoun together; and |v, p|=number oftimes the system has seen both the verb and the pronoun together.

For the selection directive in the utterance, “Select this and move itthere”, Equation (1) becomes:

$\begin{matrix}{p_{accept} = \frac{p_{select} + p_{this} + {\frac{{{select},{this}}}{2}p_{{select},{this}}}}{2 + \frac{{{select},{this}}}{2}}} & (2)\end{matrix}$

Equation (2) is also used to estimate the probability of acceptance forthe move directive, but under the assumption that there is a currentlyselected object and the user is pointing at a viable target. Theprobability of acceptance for the move directive of the utterance“Select this and move it there” has a slight subtlety but becomes:

$\begin{matrix}{p_{accept} = \frac{p_{move} + p_{it} + {\frac{{{move},{it}}}{2}p_{{select},{this}}}}{2 + \frac{{{move},{it}}}{2}}} & (3)\end{matrix}$

In the case of this directive, there are two deictic terms, but thesystem stops processing as soon as the first of the deictic terms aretranscribed. If the word “it” were mistranscribed to a non-deictic, theterm “there” would be used instead.

Equation (1) is a weighted average of the three probabilities, p_(v),p_(p) and p_(v,p) where the weighting heavily favors p_(v,p) oncesufficiently many instances of the associated {verb, pronoun} pair havebeen seen. If the system is wrong and the item is mistakenly selected,the user can do one of several things to tell the system that itsreaction was incorrect. The user can say, “no, not that,” or in fact anyshort utterance containing either the word “no” or the word “not.” Inthis case the selected item is deselected. Secondly, the user can selectsomething else. Finally, the user can do nothing with the selection andin 30 seconds the selection indicator will disappear.

In any of these cases of rejection, the associated probabilities ofp_(v), p_(p) and p_(v,p) go down, while the value of |v, p| isincremented by one. Non-rejection is assumed to be acceptance, in whichcase p_(v), p_(p) and p_(v,p) all go up, and, just like in the case ofrejection, the value of |v, p| is incremented by one. The values p_(v),p_(p) and p_(v,p) are actually maintained as pairs of integers. Forexample, p_(v) is maintained as a running fraction of the number oftimes a selection directive containing the given verb has been accepted(i.e., not rejected), over the total number of times a selectiondirective containing the given verb has been selected (i.e., eitheraccepted or rejected).

In this embodiment the system only learns from false and true positives.The user may get irritated in the case of a false negative, in otherwords, the case that a user intended to select something but the systemdid not pick up on this fact, but in the present embodiment the systemis not instrumented to pick up on such user frustration. Since thesystem cannot learn anything from false negatives, if p_(accept)>=0.5the system always opts to select. On the other hand, in an embodiment,if p_(accept)<=0.5, the system adopts a modest bandit strategy, anddraws a random number 0<q<1 and if

$\begin{matrix}{{q < \left( p_{accept} \right)^{\lg {({2 + \frac{{v,p}}{2}})}}},} & (4)\end{matrix}$

where lg( ) is the base-2 logarithm function, then the system will optto select, thereby trading off a certain amount of current reward forlearning and, hence, expected future dividends. As more instances of theparticular {verb, pronoun} pair accumulate, and hence |v, p| goes up,the less eager the system is to explore.

Undoing the move directive is handled similarly. Note, however, that itis quite frequently the case that the move directive does not contain adeictic, for example, “Move this there,” “Move this over there,” “putthis there,” and many other such examples. For this purpose the systemuses a special p_(v) _(φ) ; that gives the probability of acceptancegiven that no verb was heard, p_(v) _(φ) _(,p), giving the probabilityof acceptance of the utterance given the specified pronoun with no verb,and the analogous formula:

$\begin{matrix}{p_{accept} = \frac{p_{v_{\varnothing}} + {p_{p}\frac{{v_{\varnothing},p}}{2}p_{{v\; \varnothing},p}}}{2 + \frac{{v_{\varnothing},p}}{2}}} & (5)\end{matrix}$

Although the case where only a deictic is uttered is more common, thereare also cases where only a verb is uttered and also where only one ofthese parts of speech are uttered because of errors in the speech totext transcription. Thus, there are cases where one needs to use a termp_(p) _(φ) , analogous to p_(v) _(φ) given above.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, a schematic of an example of a cloud computingnode 510 is shown. Cloud computing node 510 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 510 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 510 there is a computer system/server 512, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 512 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 512 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 512 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 5, computer system/server 512 in cloud computing node510 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 512 may include, but are notlimited to, one or more processors or processing units 516, a systemmemory 528, and a bus 518 that couples various system componentsincluding system memory 528 to processor 516.

Bus 518 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 512 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 512, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 528 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 530 and/or cachememory 532. Computer system/server 512 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 534 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 518 by one or more datamedia interfaces. As will be further depicted and described below,memory 528 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 540, having a set (at least one) of program modules 542,may be stored in memory 528 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 542 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 512 may also communicate with one or moreexternal devices 514 such as a keyboard, a pointing device, a display524, etc.; one or more devices that enable a user to interact withcomputer system/server 512; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 512 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 522. Still yet, computer system/server 512can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 520. As depicted, network adapter 520communicates with the other components of computer system/server 512 viabus 518. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 512. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 6, illustrative cloud computing environment 650 isdepicted. As shown, cloud computing environment 650 comprises one ormore cloud computing nodes 610 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 654A, desktop computer 654B, laptop computer654C, and/or automobile computer system 654N may communicate. Nodes 610may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 650 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 654A-Nshown in FIG. 6 are intended to be illustrative only and that computingnodes 610 and cloud computing environment 650 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 650 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 760 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 762 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 764 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 766 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and enumerating and modifying cognitive interface elementsin an ambient computing environment.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present principles, as well as other variations thereof, means thata particular feature, structure, characteristic, and so forth describedin connection with the embodiment is included in at least one embodimentof the present principles. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

What is claimed is:
 1. A method, comprising receiving, by a microphoneand camera, user utterances indicative of user commands and associateduser gestures for the user utterances; parsing, by a hardware-basedrecognizer, sample utterances and the user utterances into verb partsand noun parts; recognizing, by a hardware-based recognizer, the userutterances and the associated user gestures based on the sampleutterances and descriptions of associated supporting gestures for thesample utterances, wherein said recognizing step comprises comparing theverb parts and the noun parts from the user utterances individually andas pairs to the verb parts and the noun parts of the sample utterances;and selectively performing a given one of the user commands responsiveto a recognition result.
 2. The method of claim 1, wherein saidrecognizing step comprises forming triples of a verb, a noun, and agesture from the user utterances of the user commands and the associateduser gestures for the user utterances.
 3. The method of claim 2, whereinsaid recognizing step comprises: at least one of, comparing at least oneof the verb and the noun in a triple to at least one of a verb and anoun from one or more of the sample utterances, and comparing at leastone synonym of at least one of the verb and the noun from the one ormore of the sample utterances; and determining whether the gesture inthe triple fits a description of a corresponding one or more of theassociated supporting gestures.
 4. The method of claim 2, wherein saidrecognizing step compares the verb and the noun to the gesture as a pairand individually.
 5. The method of claim 4, wherein the given one of theuser commands is selectively performed in an absence of one of the verbor the noun corresponding thereto, responsive to a match between anexisting one of the verb or the noun and a lack of contrary intentevidence that the existing one of the verb or the noun is unrelated tothe gesture.
 6. The method of claim 1, further comprising: learning frommultiple recognition sessions by acquiring user accepted examples anduser rejected examples of the user utterances and the associated usergestures; and selectively performing a given one of the user commandsresponsive to the user accepted examples and the user rejected examples.7. The method of claim 6, further comprising generating respectiveconfidence values for at least one of the noun, the verb, the gesture,and a combination thereof including at least the gesture, responsive toat least one of a number of user accepted examples and a number of userrejected examples involving the gesture and at least one of the noun andthe verb for a particular one of the user commands.
 8. The method ofclaim 7, wherein said recognizing step comprises recognizing multiplepossible intended actions, and the method further comprises arbitratingbetween the possible intended actions based on the respective confidencevalues corresponding thereto.
 9. The method of claim 6, furthercomprising generating respective error values for at least one of thenoun, the verb, the gesture, and a combination thereof including atleast the gesture, responsive to at least one of a number of useraccepted examples and a number of user rejected examples involving thegesture and at least one of the noun and the verb for a particular oneof the user commands.
 10. The method of claim 6, wherein said learningstep comprises acquiring at least one of user spoken words and userperformed gestures potentially applicable to one or more of the usercommands, for storing in a memory device as at least one of new sampleutterances and new descriptions of associated sample gestures for thenew sample utterances.
 11. The method of claim 6, wherein said learningstep: acquires a user accepted example of at least one particular userutterance and at least one particular associated user gesture responsiveto the user allowing a particular one of the user commands, representedby the at least one particular user utterance and the at least oneparticular associated user gesture, to be ultimately performed; andacquires a user rejected example of the at least one particular userutterance and the at least one particular associated user gestureresponsive to the user preventing or undoing the particular one of theuser commands represented by the at least one particular user utteranceand the at least one particular associated user gesture.
 12. The methodof claim 6, wherein said learning step comprises generating statisticaldata to inform subsequent trials based on whether the user allows thegiven one of the user commands to proceed or intends to undo the givenone of the user commands.
 13. The method of claim 6, wherein saidlearning step comprises learning one or more ways in which the userexpresses an intention to perform a particular one of the user commandsusing a combination of user gestures and deixis.
 14. The method of claim1, wherein the user commands comprise a command for moving content froma first location to a second location in a virtual environment.